Machine learning based hybrid trust management scheme for authentication and authorization in IoT

With the ongoing efforts for widespread adoption of the Internet of Things (IoT), security is one critical factor hindering the wide acceptance of IoT. To address the security issue of IoT, several studies have been carried out that involve the use of, but are not limited to, Blockchain, Artificial Intelligence (AI), and edge/fog/cloud computing. Authentication and Authorization (AA) are crucial aspects of the information security policy of the CIA triad that protect the network from malicious parties. However, existing authorization and authentication schemes are insufficient for handling security due to the IoT network’s scalability issue and the devices’ resource-constrained nature. To overcome challenges due to various constraints of IoT networks and nodes, there is a significant interest in trust management (TM) techniques to assist in the AA process for IoT. TM eliminates the requirement to determine "identities" while facilitating the authorization process. Instead, they represent security rights and constraints. This permits more flexibility and expressiveness, and standardizing current, scalable security measures. Hence, TM has received significant attention in enhancing the system’s security by defining policies and providing users with specific access rights. The current TM model in IoT is still under development, and the centralized characteristics of the IoT AA scheme are not enough to solve the heterogeneity and scalability problems. Generic TM for AA depends solely on direct inputs such as user ID and password, MAC, key, digital certificates, etc. Most common security attacks occur in the physical layer by MAC impersonation (spoofing attack), which may jeopardize the whole network. Furthermore, malicious nodes are increasingly intelligent and can change their attack approaches dynamically depending on the ambient inputs to avoid being detected. This makes attack pattern identification for the defending system difficult. Therefore, this thesis attempts to resolve this situation by proposing a holistic multilevel distributed TM scheme for trust and reputation in IoT and privacy control. Zigbee Zolertia Z1 is a popular communication node that offers coverage in a wide-area network with minimal implementation cost and power consumption. Our data-collection testbed consists of 3 client nodes and an edge or gateway node. Here, we used Zolertia Z1 low-power wireless modules compliant with IEEE 802.15.4 and Zigbee protocols. Firstly, a dataset was created from a wireless sensor network testbed comprising the node’s history of (RSSI), (LQI), MAC address, device Temperature, and battery level. Second, a multilevel TM model is designed and implemented to determine the suitable trust level for each node. The proposed scheme trained a feed-forward network and shared the weights between multi-layer perceptrons to the federated machine learning (FML) of the proposed distributed TM model to classify 4-trust levels. Once the trust level is determined, authentication and authorization access rights are intelligently determined using FML. Here, the Local trust manager, such as the edge node or gateway node, will manage the device’s access rights learning model in a distributed fashion. The Global trust manager in the cloud, on the other hand, will aggregate the device’s or edge node’s (e.g., gateway node) learning model in a centralized manner. Furthermore, intelligent attacks can be determined by the probability and frequency of the attack. The proposed TM scheme for AA in IoT allows for spoofing and impersonation attacks to be consistently detected autonomously to remove or isolate a malicious node seeking unauthorized access. Performance evaluation and benchmarking results indicate a high accuracy level compared to the currently available schemes in the literature. The proposed AA scheme’s results were achieved for the four different trust levels, with an overall accuracy of 99.7925% for different AA classes

Working together for carbon credit success

THE Prime Minister recently launched the Public-private Partnership Master Plan 2030 to strengthen the framework for public-private partnerships . Meanwhile, the first auction for Malaysian carbon credits was held by the Bursa Carbon Exchange (BCX) in July. In this instance, the Kuamut Rainforest Conservation Project is the first Malaysian carbon project to join the BCX, embarking on protecting and restoring 83,381ha of Sabah forest. It is note-worthy that the project is a public-private partnership

The traditional method of passing down the knowledge of Makyung in Kelantan

This study explores the traditional method of knowledge transmission of Kelantanese Makyung. This study examines in detail how Tok Guru teaches Makyung to students and how students learn from the Tok Guru. This study also reveals several different ways in which Makyung is traditionally taught. This study uses the methods of interview and participant observation conducted during field work with Makyung performers from the Seri Gabus Performance Group in Kampung Gabus To'Uban, Pasir Mas, Kelantan and Makyung Sri Gunong, Sri Gunong Heritage Art Association, in Gunong, Bachok, Kelantan. The findings demonstrate that there are four traditional methods of Makyung knowledge transmission, namely berguru, angin sako bako, titih seni permainan and tiru

How wind-based renewable energy contribute to CO2 emissions abatement? Evidence from Quantile-on-Quantile estimation

Renewable energy initiatives are required to achieve carbon neutrality, which is a primary goal of mitigating climate change. Renewable energy is attaining attention as it is environmentally friendly and more effective than typical forms of energy. For a sustainable environment, the latest green energy techniques like wind energy, are predominantly employed in emerging countries. The present study considered the nonlinear wind energy-CO2 emissions nexus in the top ten wind energy-consumer nations (the USA, China, India, Germany, France, Spain, the UK, Brazil, Canada, and Italy). Most previous studies use panel data tools that provide typical outcomes on the wind energy-CO2 emissions nexus, regardless of the reality that few countries have no proof of such a connection individually. The present research, conversely applies a unique econometric methodology “Quantile-on-Quantile” that can analyze time-series dependence in every nation individually to attain global yet country-specific evidence for the nexus between the variables. We specifically investigate how the wind energy quantiles impact the CO2 emissions quantiles asymmetrically by providing a suitable foundation to apprehend the overall dependent framework. The outcomes reveal that the usage of wind energy is helpful in achieving carbon neutrality at distinct quantiles in selected nations. The degree of the asymmetric association between wind energy-CO2 nexus changes by nation, requiring individual attention and awareness on the governments’ part when forming the policies of wind energy to achieve carbon neutrality

Protein expression, crystallization and in-silico studies on cytochrome P450 Oxidoreductase wildtype and mutant variants

Cytochrome P450 oxidoreductase (POR) is a crucial membrane-bound enzyme that facilitates the transfer of electrons to all cytochrome P450 (CYP450) enzymes. Several mutations in the POR gene have been reported to cause cytochrome P450 oxidoreductase deficiency (PORD), an autosomal recessive genetic disorder. This study explored the consequences of seven POR missense mutations (Y181D, A287P, R457H, R498P, C569Y, Y607C, and H628P), which have been documented in PORD patients, on the structural integrity and stability of the POR enzyme in vitro. The comparison between these mutants and the wild-type POR focused on in vitro protein expression, purification, and crystallization characteristics. The mutation-induced alterations in the POR architecture significantly influenced the protein's expression and crystallization capabilities. The magnitude of these effects on the enzyme's behavior varied from moderate to severe, contingent on the mutation's nature and position. This research illuminates the influence of specific mutations on POR stability, underlining the necessity of understanding mutation-driven effects on enzyme stability to devise personalized therapeutic approaches for PORD patients. Future studies will involve the functional characterization of these mutant enzymes to further understand their impact on the POR enzyme's activity and stability

Enhanced energy recovery of non-hazardous organic wastes via moderate pyrolysis with natural calcium- and potassium-based additives

The current research focuses on the efficiency of energy recovery from non-hazardous organic wastes. It entails the generation of low-chloride refuse-derived fuel (RDF) from municipal solid waste (MSW) through moderate pyrolysis of combustible components. The moderate pyrolysis settings and composition ratio of biogenic/non-biogenic content from MSW combination (food waste, plastic waste, and paper waste) had a significant impact on the thermochemical characteristics and fuel behavior of RDF. Furthermore, pyrolysis with optimal MSW composition was studied to reduce toxic elements (chlorides, sulphides, and nitrides) and enhance the energy value in RDF by utilizing potassium-rich (waste orange peels (OP); banana peels (BP); and maize cob (MC)) and calcium-rich species (waste animal bone meal (ABM); egg shells (ES); and mussel shells (MS)) as natural additives. The results showed that increasing the pyrolysis temperature maximized the carbon concentration with reduced oxygen moieties (low O/C ratio), indicating a high energy value as the oxygenates were eliminated during moderate pyrolysis. The RDF treated at 400 °C (SF4-400) with MSW composition of 55% non-biogenic carbon and 45% biogenic carbon resulted in HHV of 29.22 MJ kg−1, the lowest ash concentration (0.96%), and a densified microstructural surface. The addition of natural maize cob additive into MSW (SF4-400-MC) yielded the maximum HHV (33.07 MJ kg−1) with considerable reductions in chlorides (71.43%), sulphides (87.50%), and insignificant nitrides content, all of which meet ASTM RDF quality criteria Grade I

Embarking on a Journey of Reflexivity: Insights from Research Practice

The primary aim of this article is to provide an analysis of the notion of reflexivity within the context of qualitative research. This concept serves to enhance researchers' self-awareness regarding the underlying reasons for their decisions and contributes to the development of knowledge. This paper provides insights derived from the author's personal experience of composing a doctoral thesis, with respect to the reflexive practices that were duly considered. Concurrently, the objective of this article is to examine reflexivity from a pragmatic standpoint and as a means of attaining a more profound comprehension of "what you do and why you do, what you do." This paper examines the concept from both theoretical and practical perspectives for a comprehensive view. This paper points out that researchers do use reflexivity, but the key question is how much they are aware of this important practice. All in all, the concepts and reflexive practices that are discussed in this paper will serve as the impetus for one's own personal voyage of reflexivity

Quantifying carbon pool in ex-mining lake-converted constructed wetlands of Paya Indah Wetlands, Selangor, Malaysia

Ex-mining lake-converted constructed wetlands play a significant role in the carbon cycle, offering a great potential to sequester carbon and mitigate climate change and global warming. Investigating the quantity of carbon storage capacity of ex-mining lake-converted constructed wetlands provides information and justification for restoration and conservation efforts. The present study aims to quantify the carbon pool of the ex-mining lake-converted constructed wetlands and characterise the physicochemical properties of the soil and sediment. Pearson’s correlation and a one-way ANOVA were performed to compare the different sampling stations at Paya Indah Wetland, Selangor, Malaysia. An analysis of 23 years of ex-mining lake-converted constructed wetlands of Paya Indah Wetlands, Selangor, Malaysia, revealed that the estimated total carbon pool in soil and sediment accumulated to 1553.11 Mg C ha−1 (equivalent to 5700 Mg CO2 ha−1), which translates to an annual carbon sink capacity of around 67.5 Mg C ha−1 year−1. The characterisation showed that the texture of all soil samples was dominated by silt, whereas sediments exhibited texture heterogeneity. Although the pH of the soil and sediment was both acidic, the bulk density was still optimal for plant growth and did not affect root growth. FT-IR and WDXRF results supported that besides the accumulation and degradation of organic substances, which increase the soil and sediment carbon content, mineral carbonation is a mechanism by which soil and sediment can store carbon. Therefore, this study indicates that the ex-mining lake-converted constructed wetlands of Paya Indah Wetlands, Selangor, Malaysia have a significant carbon storage potential

Bridging minds and policies: supporting early career researchers in translating computational psychiatry research

A significant challenge for psychiatry is to explain precisely how the brain generates psychopathology, as its translation is presumed to advance effective mechanism-based treatments. Computational psychiatry – a mathematical understanding of mental illness – has emerged to bridge this explanatory gap [1]. Broadly, computational psychiatry uses mathematical models to study psychiatric disorders, typically done via 1) an explanatory quantitative modelling approach to explain how aberrant computations of the mind produce psychiatric symptoms, and 2) data-driven modelling, commonly used to predict and track symptom progression. These methods have been applied to identify clinically relevant markers in psychiatry [2–4]. Recently, start-ups have been applying these principles to clinical settings for aiding diagnosis (e.g., https://limbic.ai/) and delivering personalised psychotherapy (e.g., https://alena.com/). Early career researchers (ECRs) are uniquely positioned to advance the translation of computational psychiatry. However, during our own academic training, we encountered barriers that may limit its uptake amongst ECRs. Here, we highlight these barriers and propose potential solutions

Edible dragonflies and damselflies (order Odonata) as human food – A comprehensive review

The rapid growth of the human population leads to a big concern about the food y and demand worldwide. However, due to the reduction in global arable land area, humans need to find alternative food sources to fulfil their needs. Consequently, edible insects have been identified as a promising solution to ameliorate food security and increase global nutrition. Among more than 2,100 identified edible insect species, dragonflies and damselflies (order Odonata) are considered as one of nutritious food resources. Nevertheless, detailed information on the frequency and distribution of consumption of odonatans around the world is scattered and poorly documented. Based on this review, at least 61 out of 1,964 species of odonatans were reported consumed by people worldwide. The most consumed dragonflies (suborder Epiprocta; infraorder Anisoptera) are from the family of Libellulidae, followed by Aeshnidae and Gomphidae, whereas the most consumed edible damselflies (suborder Zygoptera) are from the Coenagrionidae family. Many nutrients, including proteins, lipids, energy, fibre, vitamins, and minerals are abundant in edible odonatans. Moreover, studies reported that humans employed these insects as therapeutic agents to remedy various ailments. Challenges associated with the consumption of edible odonatans include safety concerns, legal frameworks, and limited information on their bioecology which become barrier for their successful mass-rearing. However, because entomophagy is gradually gaining recognition, new and more improved methods of rearing are now being developed including for edible odonatans, encouraging sustainable insect farming. As the world strives to achieve the sustainable development goals, insect farming will pave a way for resources to be utilised for sustainable economic development